Developmental brain disorders (DBD) describe a wide range of developmental and psychiatric disorders,), that are etiologically heterogeneous with variable impacts on neurodevelopmental functioning. While specific manifestations of DBD have historically been categorized as distinct developmental and psychiatric diagnoses (e.g., autism spectrum disorders (ASD) and schizophrenia (SCZ)), there is significant clinical overlap among the behavioral features of these disorders. In addition, genomics research has shown that different DBDs can be caused by the same genomic changes, such as copy number variants (CNVs). Here we will use a genome- first approach to explain the phenotypic variability in individuals with a DBD and a de novo CNV. We will use the Research Domain Criteria (RDoC) framework to redefine overlapping clinical categories by examining the genomic components of DBD that influence neurodevelopmental functioning through the following specific aims: 1) Ascertainment of a DBD cohort with pathogenic, de novo CNVs for RDoC dimensional trait analysis. We will recruit families through our established neurodevelopmental clinics as well as through online resources. We have defined 25 recurrent CNVs that confer significant risk for DBD. We will enroll 250 quads, including a proband with a de novo CNV, the unaffected parents and an unaffected sibling. We have also selected five of the CNVs for targeted enrollment (deletion (del) 1q21.1, del16p11.2, del17q12, del22q11.2 and duplication (dup) 15q11.2q13) based on their increased, yet differential risk for ASD, SCZ or both. For each of these targeted CNVs, we will recruit 50 quads. 2) Online phenotyping across RDoC domains and constructs of probands with de novo CNVs and their family members. We will analyze family members using a novel online phenotyping assessment battery based on quantitative, dimensional traits reflecting four RDoC domains (Cognitive Systems, Social Process Systems, Negative Valence Systems and Positive Valence Systems). We predict that, across domains, de novo CNVs will have a deleterious impact on the neurodevelopmental profile of the probands, and that the degree of impact will be predicted by bi-parental performance on the corresponding domains. 3) Identification of quantitative genomic contributors to variable expressivity. We will use whole exome sequencing (WES) in our cohort of 500 quads to detect additional genomic variants (copy number or sequence-level) that, in combination with the de novo CNV, may influence the phenotypic presentation of the proband. We hypothesize individuals with multiple genomic variants will exhibit a more severe phenotype relative to those individuals with only a single CNV. Overall, by using an RDoC-based approach, this project will quantify the impact of de novo CNVs on observed versus expected levels of neurodevelopmental functioning. Furthermore, by complementing our phenotypic assessments with WES to look for additional deleterious genomic variants, we may identify genomic modifiers that explain the phenotypic variability observed across probands.